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Guo-Hua Feng,Fu-Sheng Wang,Ji-Yuan Zhang,Qing-Lei Zeng,Lei Jin,Junliang Fu,Bin Yang,Ying Sun,Tianjun Jiang,Xiangsheng Xu,Zheng Zhang,Jinhong Yuan,Liyuan Wu 한국분자세포생물학회 2013 Molecules and cells Vol.36 No.4
Interleukin-21 (IL-21)+CD4+ T cells are involved in the immune response against hepatitis B virus (HBV) by secreting IL-21. However, the role of IL-21+CD4+ T cells in the immune response against chronic hepatitis C (CHC) virus infection is poorly understood. This study aimed to investigate the role of IL-21+CD4+ T cells in CHC patients and the potential mechanisms. The study subjects in-cluded nineteen CHC patients who were grouped by viral load (low, < 106 RNA copies/ml, n = 8; high, > 106 RNA copies/ml, n = 11). The peripheral frequency of HCV-specific IL-21+CD4+ T cells was higher in the low viral load group and was negatively correlated with the serum HCV RNA viral load in all CHC patients. Meanwhile, IL-21+ cells accumulated in the liver in the low viral load group. In vitro, IL-21 treatment increased the expression of proliferation markers and cytolytic molecules on HCV-specific CD8+ T cells. In summary, these findings suggest that HCV-specific IL-21+CD4+ T cells might contribute to HCV control by rescuing HCV-specific CD8+ T cells in CHC patients.
SVM-Based Incremental Learning Algorithm for Large-Scale Data Stream in Cloud Computing
( Ning Wang ),( Yang Yang ),( Liyuan Feng ),( Zhenqiang Mi ),( Kun Meng ),( Qing Ji ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.10
We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today`s data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.